{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "任务：基于transfer_data.csv数据，建立mlp模型，再实现模型迁移学习：\n",
    "1.实现x对y的预测，可视化结果\n",
    "2.基于新数据transfer_data2.csv，对前模型进行二次训练，对比模型训练次数少的情况下的表现\n",
    "备注：模型结构：mlp，两个隐藏层，每层50个神经元，激活函数relu，输出层激活函数linear，迭代次数：100次"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "@Author  : Flare Zhao\n",
    "@Email: 454209979@qq.com\n",
    "@QQ讨论群：530533630  申请加群的验证信息为订单号（粘贴号码数字即可）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.9</td>\n",
       "      <td>24.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.8</td>\n",
       "      <td>23.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.7</td>\n",
       "      <td>22.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.6</td>\n",
       "      <td>21.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x      y\n",
       "0 -5.0  25.00\n",
       "1 -4.9  24.01\n",
       "2 -4.8  23.04\n",
       "3 -4.7  22.09\n",
       "4 -4.6  21.16"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('transfer_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    25.00\n",
       "1    24.01\n",
       "2    23.04\n",
       "3    22.09\n",
       "4    21.16\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define x and y\n",
    "X = data.loc[:,'x']\n",
    "y = data.loc[:,'y']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the data\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure(figsize=(7,5))\n",
    "plt.scatter(X,y)\n",
    "plt.title('y vs x')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101, 1) (101,)\n"
     ]
    }
   ],
   "source": [
    "X = np.array(X).reshape(-1,1)\n",
    "print(X.shape,y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 50)                100       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 50)                2550      \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 2,701\n",
      "Trainable params: 2,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "model1 = Sequential()\n",
    "model1.add(Dense(units=50, input_dim = 1, activation='relu'))\n",
    "model1.add(Dense(units=50,activation='relu'))\n",
    "model1.add(Dense(units=1, activation = 'linear'))\n",
    "model1.compile(optimizer='adam',loss='mean_squared_error')\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "101/101 [==============================] - 0s 129us/step - loss: 1.0078\n",
      "Epoch 2/200\n",
      "101/101 [==============================] - 0s 109us/step - loss: 0.9917\n",
      "Epoch 3/200\n",
      "101/101 [==============================] - 0s 109us/step - loss: 0.9843\n",
      "Epoch 4/200\n",
      "101/101 [==============================] - 0s 119us/step - loss: 0.9604\n",
      "Epoch 5/200\n",
      "101/101 [==============================] - 0s 148us/step - loss: 0.9504\n",
      "Epoch 6/200\n",
      "101/101 [==============================] - 0s 129us/step - loss: 0.9247\n",
      "Epoch 7/200\n",
      "101/101 [==============================] - 0s 139us/step - loss: 0.9163\n",
      "Epoch 8/200\n",
      "101/101 [==============================] - 0s 168us/step - loss: 0.9000\n",
      "Epoch 9/200\n",
      "101/101 [==============================] - 0s 109us/step - loss: 0.8704\n",
      "Epoch 10/200\n",
      "101/101 [==============================] - 0s 139us/step - loss: 0.8563\n",
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      "Epoch 96/200\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/200\n",
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      "Epoch 161/200\n",
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      "Epoch 169/200\n",
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      "Epoch 170/200\n",
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      "Epoch 171/200\n",
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      "Epoch 173/200\n",
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      "Epoch 174/200\n",
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      "Epoch 175/200\n",
      "101/101 [==============================] - 0s 129us/step - loss: 0.0943\n",
      "Epoch 176/200\n",
      "101/101 [==============================] - 0s 99us/step - loss: 0.0908\n",
      "Epoch 177/200\n",
      "101/101 [==============================] - 0s 89us/step - loss: 0.0976\n",
      "Epoch 178/200\n",
      "101/101 [==============================] - 0s 99us/step - loss: 0.0926\n",
      "Epoch 179/200\n",
      "101/101 [==============================] - 0s 109us/step - loss: 0.0834\n",
      "Epoch 180/200\n",
      "101/101 [==============================] - 0s 99us/step - loss: 0.0892\n",
      "Epoch 181/200\n",
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      "Epoch 182/200\n",
      "101/101 [==============================] - ETA: 0s - loss: 0.072 - 0s 109us/step - loss: 0.0859\n",
      "Epoch 183/200\n",
      "101/101 [==============================] - 0s 139us/step - loss: 0.0818\n",
      "Epoch 184/200\n",
      "101/101 [==============================] - 0s 99us/step - loss: 0.0847\n",
      "Epoch 185/200\n",
      "101/101 [==============================] - ETA: 0s - loss: 0.130 - 0s 129us/step - loss: 0.0893\n",
      "Epoch 186/200\n",
      "101/101 [==============================] - 0s 139us/step - loss: 0.0797\n",
      "Epoch 187/200\n",
      "101/101 [==============================] - 0s 129us/step - loss: 0.0902\n",
      "Epoch 188/200\n",
      "101/101 [==============================] - 0s 119us/step - loss: 0.0784\n",
      "Epoch 189/200\n",
      "101/101 [==============================] - 0s 119us/step - loss: 0.0843\n",
      "Epoch 190/200\n",
      "101/101 [==============================] - 0s 99us/step - loss: 0.1032\n",
      "Epoch 191/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101/101 [==============================] - 0s 99us/step - loss: 0.0852\n",
      "Epoch 192/200\n",
      "101/101 [==============================] - 0s 99us/step - loss: 0.0742\n",
      "Epoch 193/200\n",
      "101/101 [==============================] - 0s 119us/step - loss: 0.0823\n",
      "Epoch 194/200\n",
      "101/101 [==============================] - 0s 178us/step - loss: 0.0737\n",
      "Epoch 195/200\n",
      "101/101 [==============================] - 0s 198us/step - loss: 0.0733\n",
      "Epoch 196/200\n",
      "101/101 [==============================] - 0s 129us/step - loss: 0.0830\n",
      "Epoch 197/200\n",
      "101/101 [==============================] - 0s 119us/step - loss: 0.0789\n",
      "Epoch 198/200\n",
      "101/101 [==============================] - 0s 119us/step - loss: 0.0702\n",
      "Epoch 199/200\n",
      "101/101 [==============================] - 0s 119us/step - loss: 0.0745\n",
      "Epoch 200/200\n",
      "101/101 [==============================] - 0s 139us/step - loss: 0.0761\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x26323b2f518>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train the model\n",
    "model1.fit(X,y,epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make prediction\n",
    "y_predict = model1.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(7,5))\n",
    "plt.scatter(X,y)\n",
    "plt.plot(X,y_predict,'r')\n",
    "plt.title('y vs x(epochs=100)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=(7,5))\n",
    "plt.scatter(X,y)\n",
    "plt.plot(X,y_predict,'r')\n",
    "plt.title('y vs x(epochs=200)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4 = plt.figure(figsize=(7,5))\n",
    "plt.scatter(X,y)\n",
    "plt.plot(X,y_predict,'r')\n",
    "plt.title('y vs x(epochs=400)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\niubi\\Anaconda3\\envs\\env_ttest\\lib\\site-packages\\sklearn\\externals\\joblib\\__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(model1,'model1.m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = joblib.load('model1.m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x2</th>\n",
       "      <th>y2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.9</td>\n",
       "      <td>25.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.8</td>\n",
       "      <td>24.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.7</td>\n",
       "      <td>23.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.6</td>\n",
       "      <td>22.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x2     y2\n",
       "0 -5.0  26.00\n",
       "1 -4.9  25.21\n",
       "2 -4.8  24.44\n",
       "3 -4.7  23.69\n",
       "4 -4.6  22.96"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = pd.read_csv('transfer_data2.csv')\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define x2 and y2\n",
    "X2 = data2.loc[:,'x2']\n",
    "y2 = data2.loc[:,'y2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101, 1)\n"
     ]
    }
   ],
   "source": [
    "X2 = np.array(X2).reshape(-1,1)\n",
    "print(X2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2_predict = model2.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig5 = plt.figure(figsize=(7,5))\n",
    "plt.scatter(X,y,label = 'data1')\n",
    "plt.scatter(X2,y2 , label = 'data2')\n",
    "plt.plot(X,y2_predict,'r',label='predict2')\n",
    "plt.title('y vs x')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 0s 99us/step - loss: 2.4736\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 0s 89us/step - loss: 2.1672\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 0s 79us/step - loss: 1.8986\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 0s 79us/step - loss: 1.6712\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 0s 99us/step - loss: 1.4537\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 0s 69us/step - loss: 1.2996\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 0s 89us/step - loss: 1.1602\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 0s 99us/step - loss: 1.0358\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 0s 99us/step - loss: 0.9541\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 0s 69us/step - loss: 0.8581\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 0s 119us/step - loss: 0.7655\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 0s 89us/step - loss: 0.6968\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 0s 89us/step - loss: 0.6225\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 0s 99us/step - loss: 0.5664\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 0s 139us/step - loss: 0.5114\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 0s 107us/step - loss: 0.4710\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 0s 109us/step - loss: 0.4362\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 0s 99us/step - loss: 0.4037\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 0s 109us/step - loss: 0.3772\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 0s 89us/step - loss: 0.3519\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x26327bf40b8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transfer learning\n",
    "model2.fit(X2,y2,epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2_predict = model2.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.figure(figsize=(7,5))\n",
    "plt.scatter(X,y,label = 'data1')\n",
    "plt.scatter(X2,y2 , label = 'data2')\n",
    "plt.plot(X,y2_predict,'r',label='predict2')\n",
    "plt.title('y vs x(epochs=10)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig7 = plt.figure(figsize=(7,5))\n",
    "plt.scatter(X,y,label = 'data1')\n",
    "plt.scatter(X2,y2 , label = 'data2')\n",
    "plt.plot(X,y2_predict,'r',label='predict2')\n",
    "plt.title('y vs x(epochs=20)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig8 = plt.figure(figsize=(7,5))\n",
    "plt.scatter(X,y,label = 'data1')\n",
    "plt.scatter(X2,y2 , label = 'data2')\n",
    "plt.plot(X,y2_predict,'r',label='predict2')\n",
    "plt.title('y vs x(epochs=40)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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    "基于新数据的迁移学习实战summary：\n",
    "1、通过使用新数据，实现了模型的2次训练，达到了较好的预测效果；\n",
    "2、建立mlp模型实现了非线性分布数据的回归预测；\n",
    "3、掌握了模型存储与加载的方法；\n",
    "4、通过迁移学习，可减少模型训练迭代次数，大幅缩短训练时间"
   ]
  }
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